Reinforcement learning-There are some amazing answers.

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Reinforcement Learning is learning what to do — how to map situations to actions — so as to maximize a numerical reward signal. A learning agent can take actions that affect the state of the environment and have goals relating to the state of the environment.

There are some amazing answers.

Suppose you have a dog that is not so well trained. Every time the dog messes up the living room you reduce the amount of tasty foods you give it (punishment) and every time it behaves well you double the tasty snacks (reward). What will the dog eventually learn? Well, that messing up the living room is bad.

This simple concept is powerful. The dog is the agent, the living room the environment, you are the source of the reward signal (tasty snacks). You are giving feedback to the dog. But this feedback is vague it doesn’t mean anything without the context. So eventually the dog’s neural networks figure out the relationship between the tasty snacks and good behavior.

So in order for the dog to maximize the goal of eating more tasty snacks, it will simply behave well. And never to mess with the living room again. So you can apply RL to non-computer related problems, such as this dog-living room example. Every biological entity has reinforcement learning (RL) built in, humans, cats and many more use it. That is why RL, if solved, can be a very powerful tool for artificial intelligence (AI) applications in fields like self-driving cars.

So in Reinforcement Learning

we want to mimic the behavior of biological entities. A robot can be the agent and the goal for it will be to find the best way to move from one place in the house to the other without hitting into obstacles. So it is important to define a score, hit an obstacle and get a negative score (punishment), avoid an obstacle and get a positive score (reward). And the more distance it covers the more the reward. So feedback can come from multiple sources. The goal is to maximize the overall perceive score in every case.

The agent can always act on the environment. But it needs to find the best sets of actions to act on the environment in order to maximize that reward. This is why RL is important for self-adapting systems. Such as in AlphaGo, after a supervise phase of learning, AlphaGo play against it’s earlier self using RL to further improve on it’s own.


Robotic control systems can learn, using RL, how to move the robot arm in order to pick up objects for example. They can learn to move around the environment about object avoidance using RL. They can learn a multitude of control tasks this way, such as balancing.

RL can also be useful in game playing agents. Given the controls, the game environment and the score. The goal is to maximize the score and RL can help the agent figure out which action patterns lead to the best score. It may not be the best solution. But can be good enough and can almost always become better with more iterations.

There are many applications of RL and since deep learning (DL) is becoming more mainstream. There is now heavy research in deep RL such as at DeepMind. It is for training a variety of game playing agents as a way to get to artificial general intelligence (AGI).

So RL makes it possible to define vague goals and let the agent learn on it’s own by observing and acting on the environment to get the feedback. It is the route to AGI but it is currently notoriously hard to train these systems, we have much to learn from the dog I guess :).

Hope this helps.

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IoT- Things you need to know about Internet of Things

Posted Posted in Artificial Intelligence, Computer technology, Innovations, Mobile Technology

What is IoT in Simple?

IoT stands for Internet of Things. It’s simply means ‘An Internet connected network of things, items or people that is able to exchange data or information’.

Zaptox explains IoT

IoT is basically a methodology. It relates to devices, objects, animals or people with unique identifiers. It also able to transfer the data and information over a network without requiring human-to-human or human-to-computer interaction. An IoT system consists of sensors/devices which “talk” to the cloud through some kind of connectivity. Once the data gets to the cloud, software processes it and then might decide to perform an action. such as sending an alert or automatically adjusting the sensors/devices without the need for the user.

For example, our phone is a device that has multiple sensors. Such as GPS, accelerometer, camera but our phone does not simply sense things. The most rudimentary step will always remain to pick and collect data from the surrounding environment be it a standalone sensor or multiple devices.

IoT could also be used to solve problems such as city parking, traffic lights control, and even toll collection services. IoT through use of various sensors is currently active in agriculture to monitor humidity, temperature, pH levels, wind speed, rainfall and even pest infestation in crops.

How Internet Of Things(IoT) is useful?

  • Gathers Useful Data: The Internet of Things enables you to gather data from objects through sensors, and control them through actuators. However, the most popular use of IoT is the Industrial IoT, as you can see IDC’s IoT investment forecast.
  • Monitoring the data: One of the most apparent advantages of IoT is monitoring. It provides an advantage of knowing things in advance. With this, the exact quantity of supplies, water distribution and consumption, intelligent energy management, and security distribution gets collected easily.
  • Enhancing Device Communication: IoT enables the communication between devices. It is a Machine-to-Machine interaction. Though some people say the system could be difficult to handle once it malfunctioned, the connection of physical devices indicates the opposite. There’s an available total precision that supports lessens machines’ inefficiencies.
  • Focuses on automation and control: Due to physical objects getting connect and control digitally with wireless support, a large amount of control and automation is attainable. Without human intervention, the machines can lead a faster and timely output. Therefore, it fills some of the disorganized gaps of the machine and human interactions.

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Best AI trends for 2020

Posted Posted in Artificial Intelligence, Computer technology, Innovations, Mobile Technology

AI trends for 2020

Future AI

As you know that Technology has endorsed a striking rise in the applications based on Artificial Intelligence in the last year. With its strong impact on the software industry, it also shows enhancements in the fields of healthcare, automobile industry, agriculture, etc. The same thing is to keep progressing in the current year and future years as well. Due to this hike, companies start investing their time in the research and development process of AI. For the benefit of the environment and consumers will now feel connected with AI.

There are the 4 best AI trends for 2020 you need to get through

AI power chip

Artificial Intelligence-power chips are now aiming to be form by many of the chip producer companies like Intel, Nvidia. This is for the speed-up of AI-base application’s operations. AI confide on the particular processors that counterpart the CPU. As well as the training speed of the AI model can’t be enhanced by CPU. It can be a challenging task for the CPU to accompaniment such applications.

As a result, this AI model demands supplementary hardware for processes like a facial reorganization or object identification. These processes requires tough mathematical calculations to be done in parallel. AI chips will help in the upgrade of the performance as it designs to speed up the execution of applications. These apps base on Artificial Intelligence. You can use these chips for the processes related to speech reorganization, computer vision, etc.  Applications of healthcare or automobile industries can also utilize these chips for intelligence delivery to end-users. These chips will also help in speeding up the query processing & analytics for next-generation databases.

AI and IoT Union

AI and IoT

In the present year, both AI and IOT will meet to deliver something different. Internet of things come up with the changing concept for many of the industries. And now its union with AI will have a useful impact on our lives. What IoT can be used for is oddity detection, root cause analysis, anticipating equipment maintenance, etc.

Now AI is being unified with IoT for some uses. Like machinery problem detection, prior issue detection, & situation analysis so that better steps should be taken in the future.  Also, industrial operations can now be improved by deep neural networks and problems. For Example: traffic blockage can be solved by sovereign driving use of AI. Advance deep neural-base models can deal with video frames. As well as speech synthesis, time-series data, and unstructured data which cameras, microphones and other sensors generate. So, it will not be wrong to say that IoT is on its way to becoming the biggest driver of Artificial Intelligence. In 2020, edge devices will be rigged with the special AI chips, which are also coming in 2020 as a new trend.

Facial Reorganization

AI Face Recognition

It has gained immense popularity from few years and some even call it the future of Artificial Intelligence. The facial reorganization is analyzed as an application of Artificial Intelligence. 2030 ensures the colossal rise of this application. It helps in identifying the person with their digital image or patterns of their facial features. Facial reorganization proves useful for the security agencies and they can diagnose society’s swindlers. It also helps businesses to provide embody customer service. Where it can prove more beneficial is for biometric identification. Some other applications of it can be payment processing by security checks, law imposition, healthcare field in clinical trials and medical diagnostic processes. Though it has also faced some negative deals in the past time it has not much affected its growing popularity. Its popularity keeps on rising with time and it is proven assured results in applications.

Automated Machine Learning

Automated Machines Learning is observed to be beneficial for business analytics. And it is going to transform this as well as the next coming years. It is capable to change the face of machine learning. With this, business analysts and developers allow to unfold machine learning models to address tough schemes. It’s by eliminating the typical process of machine learning model training. Automate Machine Learning can help in solving difficult problems without following the traditional process of manual machine training. It saves efforts and time of analysts as they will be able to focus on main issues. Instead of the training process or workflow. Automate machine learning fits perfectly between intellectual API and custom machine learning platforms. You will get an accurate level of customization without getting involve in the workflow procedure. Automated Machine Learning uncovers a similar flexibility degree with a slight difference in custom data integration with mobility.

Above are some of the best key technology trends of Artificial Intelligence for 2020. As a summary, the fame of AI and its uses is increasing productively along with its integration with IoT and machine learning. The technology experts keep on researching more about Artificial Intelligence and are putting up their best efforts. Artificial Intelligence will have a great effect on various industries and businesses globally and will elevate mankind to a new productivity level. In short, AI is compelling impact from business to the IT support industry.

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Posted Posted in Artificial Intelligence, Computer technology, Innovations, Mobile Technology

Introduction-Automation or Automatic control is the creation of technology. It actually performs humans tasks. Automatic control is basically making a hardware or software that is capable of doing things automatically without human intervention. Additionally, it is the use of various control systems for operating equipment. Such as machinery, process in factories, boilers and heat treating ovens etc.

Zaptox explains Automation


Automatic control perform in several industries. For example; in the Information Technology domain, a software script can test a software product and generate a report. Moreover, some tools are also available that can generate the code for application. And user only need to configure the tool and define the process. It greatly improve productivity, saves time and cutting cost. Additionally, Artifial Intelligence is all about to make machines or software emulate, and eventually supersede human behavior and intelligence. Automatic control may or may not be based on Artificial Intelligence.


There is a difference between Automatic control and Artificial Intelligence.

  • Automatic control is basically hardware or software that mimics human actions.
  • Whereas, Artificial Intelligence is the simulation of human intelligence by machines.

Clearly, AI tells you how to do your job. Whereas automatic control gets to the point and take it away.

Types of Automation

Keep reading for its types

Network Automation(NA)

The process of automating the configuration, management and operations of a computer network is known as Network Automatics. For Example, Firewall application , which once set up,automatically allows or blocks incoming/outgoing traffic for abnormalities.


Office Automation(OA)

OA refers to the collective hardware, as well as, software. And then process that enable automatic control of information processing and communication tasks of an organization. Moreover, It includes,

  • Software that enable word processing, creating spreadsheets as well as managing accounts and more
  • internet connectivity and Email programs to send as well as receive email messages.
  • instant communication, such as VoIP and more.

Business Process Automation(BPA)

BPA, actually, is the process of managing information, data. And then process to reduce costs, resources as well as investment. Furthermore, BPA increase productivity by automating key key business process through computing technology.

Additionally, It has three fundamental principles:

Orchestration: It allows organizations to builds systems that provide centralized management of enterprise computing architecture.

Integration: Amalgamates business functions by ensuring the BPA system is spread across the process-centric boundaries of an organization.

Automated Execution: reduces multiple tasks with minimal human intervention.

Application Release Automation

ARA is the process of modeling, as well as, deploying software products. Then configure them for java and other platforms. Moreover, different types of ARA may include: process-based, packaged based, declarative and imperative solutions and approaches.

So, different types of approaches for ARA have different benefits. For Example; a package-based approach often succeeds in automating the server layer of deployment process. This approach can collectively handle much more of work. Whereas, a declarative-based approach may mean more attention to the application layer of the process. Mean-while, in an imperative -based ARA approach , developers may focus on particular programming languages and commands.

Robotic Process Automation

RPA is the type of Automation. It is basically use of software with Artificial Intelligence and machine learning capabilities to handle high-volume repeatable tasks that previously performed by humans. Moreover, Robotic Process Automation is uses artificial intelligence to build software robots that automate tasks that once required human intervention, such as customer service and IT management.

Decision Automation

Decision Automation is also the type of Automation. Simply, it is the use of software to automatically make choices in business. It is distinct from the Decision Support System because the software can actually makes decisions instead of just offering information to humans who then make decisions. It makes choices on pre programmed business rules. Let me clarify with a diagram.

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Natural Language Process(NLP)

Posted Posted in Artificial Intelligence, Computer technology, Innovations, Mobile Technology

Definition- NLP (Natural Language Process) refers to AI method. It helps to interacting as well as communicating with an intelligent system using natural language such as English. In general, NLP involves machines or robots to understand and process the language that human speak.

For Example: you want an intelligent system. Suppose, robot to perform as per your instructions. And you want hear decision from a dialogue based expert system,then Process of Natural Language is required.

Components of Natural Language Process (NLP)

So, keep reading, i am going to explain major components of NLP

  1. NLU (Natural Language Understanding)
  2. NLG (Natural Language Generation)

Natural Language Understanding:-

Basically, NLU is the component of NLP . As well as it uses computer software to understand input. That is made in the form of sentences in text or speech format.

Also, you can say that, it is use to

  • Mapping the input in natural language into useful representation.
  • Analyzing different aspects of language.

NLU has some problems, i have explained below. Stay with me,

Natural Language Generation:-

NLG is the process of creating meaningful sentences. These sentences are in the form of natural language from some internal representation. Moreover, It includes:

  • Text planning:- text planning means retrieving the relevant content from knowledge base.
  • Sentence planning:- includes selecting required words, forming meaningful sentences,and set the tone of sentence.
  • Text realization:- So, text realization involves mapping sentence plan into sentence structure.

Problems in NLU

NLU is very ambiguous. It has different levels are as follow

Lexical ambiguity : basically, it is the word-level ambiguity. For example, treating the word “board” as noun or verb?

Syntax level ambiguity: So, it means a sentence can be pars in different ways. For Example, “She lifted the beetle with red cap”. Did he use cap to lift the beetle or he lifted a beetle that had red cap?

Referential ambiguity: As well as, it means referring to something using pronouns. For example: Ailie went to Shiza. She said, “I am tired”. Actually who is tired?

Steps in Natural Language Process

Steps in Natural Language Process NLP

Let me explain in detail:

Lexical analysis: involves identifying and analyzing the structure of words. Lexicon of a language indicates the collection of words and phrases in language. Moreover, Lexical analysis is dividing the whole chunk of text into paragraphs, sentences and words.

Syntactic Analysis: basically, it includes analysis of words in the sentence for grammar. And then arranging words in manner that shows the relationship among words.

Semantic analysis: 

it actually creates the exact meaning or the dictionary meaning from the text. It is done by mapping syntactic structures and objects in the task domain. The semantic analyzer disregards sentence such as “hot ice-cream”.

Discourse Integration:

means any sentence depends upon the meaning of sentence just before it. In addition, it also brings about the meaning of immediately succeeding sentence.

Programmatic analysis:

means, what was said is re-interpreted on what is actually meant. Further more, it involves deriving those aspects of language which require real world knowledge.

Implementation of Syntactic Analysis

There are number of algorithms for syntactic analysis, here, we consider only simple methods, such as:

  • Context-Free Grammar
  • Top-Down Parser

Let me explain in detail

Context-Free Grammar

It consists rules with a single symbol on the left-hand side of rewrite rules.

For Example:

Let me create grammar to parse the sentence “The bird pecks the grains”

Articles(DET) : a|an|the

Nouns: bird|birds|grain|grains

Noun phrase(NP): Article+noun|article+adjective+noun

Verbs: pecks|pecked|pecking

Verb phrase(VP): noun phrase+ verb|verb+noun phrase

Adjectives(ADJ): beautiful|small|chirping

The parse tree breaks down the sentence into structure parts so that the computer can easily understand and process it. In order for parsing algorithm to construct this parse tree. And a set of rewrite rules need to be construct. That rules describe what tree structures are legal to rewrite. Further more, these rules say that a certain symbol may be expand in the tree by a sequence of other symbols.

For Example:

Rewrite rules for the sentence are as follow:

S -> NP VP


VP -> V NP


DET -> a|the

ADJ -> beautiful | perching

N -> bird | birds | grain | grains

V -> peck | pecks | pecking

And the creation of parse tree:

Natural Language Process NLP parsing tree

Top-Down Parser

Parsing is a frequently use term both in the realm of data quality, and in computing in general. So, the parser starts with S symbol and attempts to rewrite it into a sequence of terminal symbols. Terminal symbol matches the classes of words in the input sentence until it consists entirely of terminal symbols.

Then, these are check with the input sentence to see if it match. If not, the process starts over again with different set of rules. It repeats until to reach the specific rule and describes the structure of sentence.

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